# Global visual localization in LiDAR-maps through shared 2D-3D embedding space

arXiv: https://arxiv.org/abs/1910.04871 · HTML: https://mlsanity.com/paper/1910.04871/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 28 of 28)

- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization](https://mlsanity.com/paper/2312.16648/) (2023)
- [(LC)2: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition](https://mlsanity.com/paper/2304.08660/) (2023)
- [I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections](https://mlsanity.com/paper/2303.01043/) (2023)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization](https://mlsanity.com/paper/2306.11346/) (2023)
- [EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization](https://mlsanity.com/paper/2309.07471/) (2023)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [VOLoc: Visual Place Recognition by Querying Compressed Lidar Map](https://mlsanity.com/paper/2402.15961/) (2024)
- [Contrastive Learning of Features between Images and LiDAR](https://mlsanity.com/paper/2206.12071/) (2022)
- [RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps](https://mlsanity.com/paper/2309.09875/) (2023)
- [VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition](https://mlsanity.com/paper/2403.14594/) (2024)
- [ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition](https://mlsanity.com/paper/2403.18762/) (2024)
- [Monocular Visual Place Recognition in LiDAR Maps via Cross-Modal State Space Model and Multi-View Matching](https://mlsanity.com/paper/2410.06285/) (2024)
- [VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models](https://mlsanity.com/paper/2603.09826/) (2026)
- [TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes](https://mlsanity.com/paper/2412.10308/) (2024)
- [Range and Bird's Eye View Fused Cross-Modal Visual Place Recognition](https://mlsanity.com/paper/2502.11742/) (2025)
- [Model Guided Road Intersection Classification](https://mlsanity.com/paper/2104.12417/) (2021)
- [Identifying High Accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-Based Method](https://mlsanity.com/paper/2106.14049/) (2021)
- [Optimization of Directional Landmark Deployment for Visual Observer on SE(3)](https://mlsanity.com/paper/2203.14485/) (2022)
- [Uncertainty-Aware DNN for Multi-Modal Camera Localization](https://mlsanity.com/paper/2211.01234/) (2022)
- [Attention-Enhanced Cross-modal Localization Between 360 Images and Point Clouds](https://mlsanity.com/paper/2212.02757/) (2022)
- [Single-Shot Global Localization via Graph-Theoretic Correspondence Matching](https://mlsanity.com/paper/2306.03641/) (2023)
- [Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures](https://mlsanity.com/paper/2412.01299/) (2024)
- [RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving](https://mlsanity.com/paper/2603.07920/) (2026)
- [GeoUniPR: A Geometry-Consistent Unified Framework for Cross-Modal Place Recognition](https://mlsanity.com/paper/2608.11263/) (2026)
- [DXPR: Depth-Based Vision-LiDAR Cross-Modal Place Recognition Using Vision Foundation Models](https://mlsanity.com/paper/2609.09005/) (2026)

## Abstract (arXiv)

Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonomous driving field. While most vision-based approaches match an image w.r.t. an image database, global visual localization within LiDAR-maps remains fairly unexplored, even though the path toward high definition 3D maps, produced mainly from LiDARs, is clear. In this work we leverage Deep Neural Network (DNN) approaches to create a shared embedding space between images and LiDAR-maps, allowing for image to 3D-LiDAR place recognition. We trained a 2D and a 3D DNN that create embeddings, respectively from images and from point clouds, that are close to each other whether they refer to the same place. An extensive experimental activity is presented to assess the effectiveness of the approach w.r.t. different learning paradigms, network architectures, and loss functions. All the evaluations have been performed using the Oxford Robotcar Dataset, which encompasses a wide range of weather and light conditions.